I think I've identified a problem with curve_fit which occurs when one
attempts to fit normally distributed data with a gaussian. From the
documentation of curve_fit, it appears that 'sigma' should be the
uncertainties on the y-values of the data; however, the following example
(see attached code) should make it clear that there's a problem with this.
My best guess is that the sigma are actually weights (=1.0/sigma). Can
anyone confirm or deny this? Seems like from the name it should be
uncertainties but from the behavior of the code it appears otherwise.
Also, I was wondering if there's a way to supply asymmetric errors to
curve_fit (or for that matter, to leastsqr or any wrapper thereof).
Thanks very much,
Michael
http://www.nabble.com/file/p22380378/testFit.py testFit.py
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